Alan Tuning

  • Student

Activity Feed

On November 14, 2018, Alan Tuning commented on Chariot: Changing Transit through Crowdsourced Co-Creation :

It’s really interesting to see what Chariot is doing. However, I wonder whether they are genuine about crowd-sourcing as a solution to solve their customer challenges as opposed to a marketing tactic to get buy-in from consumers. As a marketing tactic, I think it is fantastic. By getting users to vote on a location, they are highly likely to establish loyalty and regular use whilst also building a network of customers who serve as brand promoters.

Certainly, this approach of seeking consumer input would a great example for businesses and governments to consider. However, if they genuinely want to produce better solutions, I would challenge these organizations to identify the problems that customers are more equipped to solve and put these problems in the hands of customers.

On November 14, 2018, Alan Tuning commented on Rome Wasn’t Built in a Day (Neither was Toronto) :

Wow, this seems like an interesting – and tough – application of crowd-sourcing. It sounds like they are doing a great job of getting much more input than these types of projects typically get. You raise the great point that there are so many different ways to ‘get involved’, but that only makes it more difficult. I think a major challenge is that they need to decide on the type of input that they want from citizens. It seems like they want more than just typical consumer feedback and ideas. I think in order to add real value, they need the considered views of citizens who take into account the advantages and disadvantages of different options. This raises two further challenges. First, for citizens to provide this input, they would likely need to spend a significant amount of time understanding the issues around each decision – it is unlikely that many citizens would dedicate this time and the ones that do could represent a biased sample. Second, citizens may have in mind the solutions they think they want and these solutions may not necessarily align with the ones that they would find most effective once constructed. In a sense, this crowd-sourcing approach is opposed to a human-centered design approach because they may not be carefully designing to solve problems.

On November 14, 2018, Alan Tuning commented on Defense Distributed: Is There a Future for Gun-control? :

I’m with N. here, but I wonder whether there may still be opportunities for Defense Distributed to succeed as a business and support better gun use without increasing the distribution of illegal guns.

First, they could work with the Governments to create a legal and technological framework this is consistent with current (or proposed) gun laws. Depending on the country, this could involve locking down the technology so that it can only be used by the purchaser for the agreed quantity and requiring background checks and Government licenses to access the technology. This technology could also be used to require unique tracking numbers on each gun and unique barrel markings to be produced on used bullets. This would allow regulatory and policing agencies to monitor the use and distribution of these guns and more easily link illegal use of this guns with the purchaser of the guns.

Second, they could introduce new safety features on their guns that may not be feasible due to the variability in traditional manufacturing processes.

Third, they could innovate their product to tailor it to specific uses, where they may not necessarily be large scale. For example, they could produce designs for guns designed exclusively for farmers in specific settings.

On November 14, 2018, Alan Tuning commented on Adidas: Eliminating prototyping and seeking personalization :

I’m impressed about how fast the use of 3D printing in this industry is developing. I agree with Anonymous’ perspective on the need to carefully select the target market. However, I wonder whether they can be successful at either with the much higher price point. The shoe buying experience is still focused on trial before commitment. Beyond the innovator consumers who likely see some novelty value, I think it will take time for consumers to trust that a shoe developed for them will have the right fit and comfort when they actually use it for an extended period of time – and the consumers need to be willing to pay a premium for the shoe.

In my view, the challenge for Adidas is to solve this problem of trial-ability. Perhaps this requires significantly reducing the cost of manufacturing so that they can afford product returns or it might be that Adidas need to have trial archetypes and then apply the 3D printing to customize based on consumer feedback to a the trial.

Thanks Alex for sharing this – it’s fascinating to see how Airbnb is making this shift from human-centered design to data-driven design as they continue scaling. I can see how more basic data use can easily be combined with human-centered design. The examples you gave such as fighting fraud and improving matching appear to be data-driven solutions developed through a human-centered problem solving process. My view is that Airbnb needs to test each new data use and solution by answering two questions.

First, can the solution be explained and connected to a human problem and solution? You noted that this is a priority for engineers and that it will become more challenging as data use becomes more advanced.

Second, does the solution produce a better outcome than a human or relative to existing processes. You raised risks such as the risk of institutionalizing bias. This is an important risk that should be considered when applying data-driven solutions. However, a data-driven solution is less biased than a human, then it should be adopted.

On November 14, 2018, Alan Tuning commented on Moley the Robotic Chef: The Future of Cooking or An Expensive Toy? :

Thanks Energy for sharing this. This strikes me as an extremely interesting application of ML with the classic question of whether the robot will be able to succeed without human intervention. In this case, I would wonder what it would take for a robot to handle variability such as running out of clean dishes or ingredients. It doesn’t appear as if the ML has reached the point at which the robot can begin learning how to handle these types of situations, without instructions directly being programmed in the specific situation.

The other question I have is which market this robot would target – and different markets raise significant challenges. If the robot is tailored to the high-end restaurant market, I wonder how it could achieve the ‘art’ of the best human chefs. If the robot is tailored to a wider population, I wonder whether it would ever be cheap enough in the medium-term to justify use in a cafe or a home.